----------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/VTC_Design_kz2020demanding.l
> og
  log type:  text
 opened on:   5 Jun 2020, 09:23:08

. use "VTC_Design_kz2020demanding.dta", clear

. pause on

. 
. 
. 
. ***
. * Descriptive Statistics
. ***
. 
. * Table 2: Summary Statistics of Dependent Variables Used in Study Two
. 
. eststo clear

. estpost summarize violations_broad scope_broad subpoena preserve_evidence

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
violations~d |        75         75   .7733333   .1776577   .4214946          0          1         58 
 scope_broad |        75         75        .56   .2497297   .4997297          0          1         42 
    subpoena |        73         73   .3561644   .2324962   .4821786          0          1         26 
preserve_e~e |        71         71   .3380282   .2269618    .476405          0          1         24 

. esttab using sumstats_DV.tex, replace cells("mean(fmt(2)) min max count(fmt(0))") nonum noobs  label
(output written to sumstats_DV.tex)

. 
. * Table A3: Summary Statistics of Independent Variables Used in Study Two
. 
. eststo clear

. estpost summarize ictj v2xcs_ccsi_avg5  authoritarian_govt civil_war /*
> */ precedent_scope precedent_violations_broad precedent_subpoena precedent_preserve_evidence /*
> */ yr democracy transitional

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
        ictj |        75         75        .28   .2043243   .4520225          0          1         21 
v2xcs_ccsi~5 |        70         70   .6322345   .0544437   .2333318   .1240493   .9632879   44.25641 
authoritar~t |        75         75   .6933333   .2154955   .4642149          0          1         52 
   civil_war |        75         75   .3866667   .2403604   .4902656          0          1         29 
precedent~pe |        75         75   1.093333   2.301982   1.517228          0          7         82 
precedent_~d |        75         75   1.933333   4.144144   2.035717          0          9        145 
precedent_~a |        75         75   1.026667   2.972252   1.724022          0          7         77 
precedent~ce |        75         75        .76   1.428108   1.195035          0          4         57 
          yr |        75         75   31.13333   109.4414   10.46143          2         48       2335 
   democracy |        68         68   .6617647    .227173   .4766266          0          1         45 
transitional |        75         75        .64   .2335135   .4832324          0          1         48 

. esttab using sumstats_IV.tex, replace cells("mean(fmt(2)) min max count(fmt(0))") nonum noobs  label
(output written to sumstats_IV.tex)

. 
. 
. 
. *** 
. * Main analysis
. ***
. 
. * Setting Global Controls
. 
. global violence_type  authoritarian_govt civil_war

. global domestic_politics  transitional democracy

.  
. 
. *This makes Table 3: ICTJ Involvement and Truth Commission Powers
. 
. eststo clear

. eststo A: logit violations_broad ictj v2xcs_ccsi_avg5 $violence_type $precedents $domestic_politics yr precedent_violations_broad, vce(cluster gwn
> o)

Iteration 0:   log pseudolikelihood = -37.948628  
Iteration 1:   log pseudolikelihood = -35.887106  
Iteration 2:   log pseudolikelihood = -35.824195  
Iteration 3:   log pseudolikelihood = -35.824002  
Iteration 4:   log pseudolikelihood = -35.824002  

Logistic regression                             Number of obs     =         67
                                                Wald chi2(8)      =       5.21
                                                Prob > chi2       =     0.7349
Log pseudolikelihood = -35.824002               Pseudo R2         =     0.0560

                                                (Std. Err. adjusted for 52 clusters in gwno)
--------------------------------------------------------------------------------------------
                           |               Robust
          violations_broad |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
                      ictj |   .5448745   .9244505     0.59   0.556    -1.267015    2.356764
           v2xcs_ccsi_avg5 |   1.205963   1.349496     0.89   0.372    -1.439001    3.850927
        authoritarian_govt |   .4165475   .7978037     0.52   0.602    -1.147119    1.980214
                 civil_war |   .0380036   .7143489     0.05   0.958    -1.362095    1.438102
              transitional |   .4438171   .6404292     0.69   0.488    -.8114012    1.699035
                 democracy |  -.3560721   .7936569    -0.45   0.654    -1.911611    1.199467
                        yr |   .0278162   .0398032     0.70   0.485    -.0501966     .105829
precedent_violations_broad |   .0408323   .2554108     0.16   0.873    -.4597638    .5414283
                     _cons |  -1.033332   1.591343    -0.65   0.516    -4.152307    2.085643
--------------------------------------------------------------------------------------------

. eststo B: logit scope_broad ictj v2xcs_ccsi_avg5 $violence_type $precedents $domestic_politics yr  precedent_scope, vce(cluster gwno) /**/

Iteration 0:   log pseudolikelihood = -46.433398  
Iteration 1:   log pseudolikelihood = -35.095059  
Iteration 2:   log pseudolikelihood = -34.993975  
Iteration 3:   log pseudolikelihood = -34.993838  
Iteration 4:   log pseudolikelihood = -34.993838  

Logistic regression                             Number of obs     =         67
                                                Wald chi2(8)      =      18.52
                                                Prob > chi2       =     0.0176
Log pseudolikelihood = -34.993838               Pseudo R2         =     0.2464

                                        (Std. Err. adjusted for 52 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
       scope_broad |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
              ictj |   2.205221   .9509544     2.32   0.020     .3413845    4.069057
   v2xcs_ccsi_avg5 |  -.3876508   1.596344    -0.24   0.808    -3.516428    2.741126
authoritarian_govt |   .6546492   .8045905     0.81   0.416    -.9223192    2.231618
         civil_war |   .3055294   .6880493     0.44   0.657    -1.043022    1.654081
      transitional |  -.4195703   .6602883    -0.64   0.525    -1.713712    .8745709
         democracy |   .0316941   .7160756     0.04   0.965    -1.371788    1.435176
                yr |   .0591265   .0389529     1.52   0.129    -.0172198    .1354728
   precedent_scope |   .1327462   .2610767     0.51   0.611    -.3789547    .6444471
             _cons |  -2.497309   1.795209    -1.39   0.164    -6.015853    1.021235
------------------------------------------------------------------------------------

. eststo C: logit subpoena ictj v2xcs_ccsi_avg5 $violence_type $precedents $domestic_politics yr precedent_subpoena, vce(cluster gwno) /**/

Iteration 0:   log pseudolikelihood = -42.236682  
Iteration 1:   log pseudolikelihood = -31.039408  
Iteration 2:   log pseudolikelihood = -30.529362  
Iteration 3:   log pseudolikelihood =   -30.5245  
Iteration 4:   log pseudolikelihood = -30.524498  

Logistic regression                             Number of obs     =         65
                                                Wald chi2(8)      =      18.28
                                                Prob > chi2       =     0.0192
Log pseudolikelihood = -30.524498               Pseudo R2         =     0.2773

                                        (Std. Err. adjusted for 51 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
          subpoena |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
              ictj |   .7656237   .8765134     0.87   0.382    -.9523109    2.483558
   v2xcs_ccsi_avg5 |   3.868968   1.786054     2.17   0.030     .3683663    7.369569
authoritarian_govt |  -.6758543   .9779052    -0.69   0.489    -2.592513    1.240805
         civil_war |  -.2628366   .9198589    -0.29   0.775    -2.065727    1.540054
      transitional |   3.298076   .9286385     3.55   0.000     1.477978    5.118174
         democracy |   1.369964   .7929068     1.73   0.084    -.1841044    2.924033
                yr |  -.0773361   .0420519    -1.84   0.066    -.1597563    .0050842
precedent_subpoena |   .2356564   .2864105     0.82   0.411    -.3256979    .7970107
             _cons |  -3.930759   2.039994    -1.93   0.054    -7.929073    .0675551
------------------------------------------------------------------------------------

. eststo D: logit preserve_evidence ictj v2xcs_ccsi_avg5 $violence_type $precedents $domestic_politics yr precedent_preserve_evidence, vce(cluster g
> wno) /**/

Iteration 0:   log pseudolikelihood = -39.749528  
Iteration 1:   log pseudolikelihood = -28.485415  
Iteration 2:   log pseudolikelihood = -27.759299  
Iteration 3:   log pseudolikelihood = -27.746844  
Iteration 4:   log pseudolikelihood = -27.746827  
Iteration 5:   log pseudolikelihood = -27.746827  

Logistic regression                             Number of obs     =         64
                                                Wald chi2(8)      =      21.11
                                                Prob > chi2       =     0.0069
Log pseudolikelihood = -27.746827               Pseudo R2         =     0.3020

                                                 (Std. Err. adjusted for 51 clusters in gwno)
---------------------------------------------------------------------------------------------
                            |               Robust
          preserve_evidence |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
                       ictj |   1.888463   .9418367     2.01   0.045     .0424971    3.734429
            v2xcs_ccsi_avg5 |  -3.618228   2.417326    -1.50   0.134      -8.3561    1.119644
         authoritarian_govt |   .4853698   .8192137     0.59   0.554     -1.12026    2.090999
                  civil_war |  -1.173035   .9359514    -1.25   0.210    -3.007466    .6613957
               transitional |   1.755142   .9190982     1.91   0.056    -.0462579    3.556541
                  democracy |   2.244277   .9795849     2.29   0.022      .324326    4.164228
                         yr |   .1285906   .0491691     2.62   0.009      .032221    .2249602
precedent_preserve_evidence |   -.233573    .404895    -0.58   0.564    -1.027153    .5600067
                      _cons |   -5.68238   2.251478    -2.52   0.012     -10.0952   -1.269564
---------------------------------------------------------------------------------------------

. esttab A B C D using study_two.tex, replace unstack label b(2) se(2) /*
> */ nomtitles title("ICTJ Involvement and Truth Commission Powers") /*
> */ addnotes("All models report clustered standard errors by country.") star(+ 0.10 * 0.05 ** 0.01) 
(output written to study_two.tex)

. 
. 
. 
. *** 
. * Predicted Effects, Substantive Effects, Figures
. ***
. 
. * Coefficient Plots - Figure 8: Effect of ICTJ Involvement, with 95% CIs
. 
. sem (violations_broad <- ictj v2xcs_ccsi_avg5 $violence_type $precedents $domestic_politics yr  precedent_violations_broad, vce(cluster gwno))
(8 observations with missing values excluded)

Endogenous variables

Observed:  violations_broad

Exogenous variables

Observed:  ictj v2xcs_ccsi_avg5 authoritarian_govt civil_war transitional democracy yr precedent_violations_broad

Fitting target model:

Iteration 0:   log pseudolikelihood = -563.91783  
Iteration 1:   log pseudolikelihood = -563.91783  

Structural equation model                       Number of obs     =         67
Estimation method  = ml
Log pseudolikelihood= -563.91783

                                                  (Std. Err. adjusted for 52 clusters in gwno)
----------------------------------------------------------------------------------------------
                             |               Robust
                             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------+----------------------------------------------------------------
Structural                   |
  violations_broad <-        |
                        ictj |   .0811753   .1417304     0.57   0.567    -.1966111    .3589617
             v2xcs_ccsi_avg5 |   .2077301   .2653769     0.78   0.434     -.312399    .7278593
          authoritarian_govt |   .0628604   .1400717     0.45   0.654    -.2116751    .3373959
                   civil_war |  -.0009647   .1264875    -0.01   0.994    -.2488757    .2469462
                transitional |   .0661659    .111439     0.59   0.553    -.1522504    .2845823
                   democracy |  -.0608252   .1481484    -0.41   0.681    -.3511908    .2295404
                          yr |   .0059907   .0077805     0.77   0.441    -.0092587    .0212401
  precedent_violations_broad |   .0032253   .0388528     0.08   0.934    -.0729248    .0793754
                       _cons |   .3569464   .2917886     1.22   0.221    -.2149487    .9288415
-----------------------------+----------------------------------------------------------------
      var(e.violations_broad)|   .1776408   .0254658                      .1341276    .2352703
----------------------------------------------------------------------------------------------

. coefplot, drop(_cons) xline(0) b(b_std) v(V_std) xtitle(Standardized Coefficients) level(95)

. graph export "/Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_abuses.eps", repl
> ace
(file /Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_abuses.eps written in EPS f
> ormat)

. 
. sem (scope_broad <- ictj v2xcs_ccsi_avg5 $violence_type $precedents $domestic_politics yr  precedent_scope, vce(cluster gwno))
(8 observations with missing values excluded)

Endogenous variables

Observed:  scope_broad

Exogenous variables

Observed:  ictj v2xcs_ccsi_avg5 authoritarian_govt civil_war transitional democracy yr precedent_scope

Fitting target model:

Iteration 0:   log pseudolikelihood = -550.36103  
Iteration 1:   log pseudolikelihood = -550.36103  

Structural equation model                       Number of obs     =         67
Estimation method  = ml
Log pseudolikelihood= -550.36103

                                          (Std. Err. adjusted for 52 clusters in gwno)
--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
Structural           |
  scope_broad <-     |
                ictj |   .4151473   .1325029     3.13   0.002     .1554465    .6748481
     v2xcs_ccsi_avg5 |  -.0550796   .2696824    -0.20   0.838    -.5836475    .4734882
  authoritarian_govt |   .1200187   .1376347     0.87   0.383    -.1497404    .3897777
           civil_war |   .0555594   .1245767     0.45   0.656    -.1886065    .2997254
        transitional |  -.0823991   .1266736    -0.65   0.515    -.3306748    .1658766
           democracy |  -.0043103   .1266329    -0.03   0.973    -.2525062    .2438855
                  yr |   .0107786   .0061484     1.75   0.080     -.001272    .0228292
     precedent_scope |    .022641   .0423464     0.53   0.593    -.0603564    .1056384
               _cons |   .0229473   .2776092     0.08   0.934    -.5211569    .5670514
---------------------+----------------------------------------------------------------
   var(e.scope_broad)|   .1752748   .0222898                      .1366066    .2248886
--------------------------------------------------------------------------------------

. coefplot, drop(_cons) xline(0) b(b_std) v(V_std) xtitle(Standardized Coefficients) level(95)

. graph export "/Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_antecedents.eps",
>  replace
(file /Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_antecedents.eps written in 
> EPS format)

. 
. sem (subpoena <- ictj v2xcs_ccsi_avg5 $violence_type $precedents $domestic_politics yr  precedent_subpoena, vce(cluster gwno))
(10 observations with missing values excluded)

Endogenous variables

Observed:  subpoena

Exogenous variables

Observed:  ictj v2xcs_ccsi_avg5 authoritarian_govt civil_war transitional democracy yr precedent_subpoena

Fitting target model:

Iteration 0:   log pseudolikelihood = -550.77555  
Iteration 1:   log pseudolikelihood = -550.77555  

Structural equation model                       Number of obs     =         65
Estimation method  = ml
Log pseudolikelihood= -550.77555

                                          (Std. Err. adjusted for 51 clusters in gwno)
--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
Structural           |
  subpoena <-        |
                ictj |   .0865008    .142804     0.61   0.545    -.1933899    .3663915
     v2xcs_ccsi_avg5 |   .5636046   .3071101     1.84   0.066    -.0383202    1.165529
  authoritarian_govt |  -.1216219   .1639089    -0.74   0.458    -.4428775    .1996337
           civil_war |  -.0168622   .1389151    -0.12   0.903    -.2891307    .2554063
        transitional |    .502899   .1214944     4.14   0.000     .2647743    .7410236
           democracy |   .2242446   .1401126     1.60   0.109    -.0503711    .4988603
                  yr |   -.011607   .0076855    -1.51   0.131    -.0266703    .0034563
  precedent_subpoena |    .035938   .0479962     0.75   0.454    -.0581327    .1300087
               _cons |  -.0913741   .3520737    -0.26   0.795     -.781426    .5986777
---------------------+----------------------------------------------------------------
      var(e.subpoena)|   .1608051   .0196928                      .1264906    .2044285
--------------------------------------------------------------------------------------

. coefplot, drop(_cons) xline(0) b(b_std) v(V_std) xtitle(Standardized Coefficients) level(95)

. graph export "/Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_subpoena.eps", re
> place
(file /Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_subpoena.eps written in EPS
>  format)

. 
. sem (preserve_evidence <- ictj v2xcs_ccsi_avg5 $violence_type $precedents $domestic_politics yr  precedent_preserve_evidence, vce(cluster gwno))
(11 observations with missing values excluded)

Endogenous variables

Observed:  preserve_evidence

Exogenous variables

Observed:  ictj v2xcs_ccsi_avg5 authoritarian_govt civil_war transitional democracy yr precedent_preserve_evidence

Fitting target model:

Iteration 0:   log pseudolikelihood = -505.81589  
Iteration 1:   log pseudolikelihood = -505.81589  

Structural equation model                       Number of obs     =         64
Estimation method  = ml
Log pseudolikelihood= -505.81589

                                                   (Std. Err. adjusted for 51 clusters in gwno)
-----------------------------------------------------------------------------------------------
                              |               Robust
                              |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------+----------------------------------------------------------------
Structural                    |
  preserve_evidence <-        |
                         ictj |   .3650947    .145804     2.50   0.012     .0793241    .6508654
              v2xcs_ccsi_avg5 |  -.4809218   .3271285    -1.47   0.142    -1.122082    .1602382
           authoritarian_govt |   .0604386   .1307378     0.46   0.644    -.1958028      .31668
                    civil_war |  -.1885704   .1268429    -1.49   0.137    -.4371779    .0600371
                 transitional |   .2307027   .1198782     1.92   0.054    -.0042543    .4656597
                    democracy |   .3090102   .1281469     2.41   0.016     .0578469    .5601735
                           yr |    .015599   .0053596     2.91   0.004     .0050943    .0261037
  precedent_preserve_evidence |  -.0384044    .064665    -0.59   0.553    -.1651454    .0883366
                        _cons |  -.2589924   .2552463    -1.01   0.310     -.759266    .2412811
------------------------------+----------------------------------------------------------------
      var(e.preserve_evidence)|   .1437104    .025606                      .1013499    .2037762
-----------------------------------------------------------------------------------------------

. coefplot, drop(_cons) xline(0) b(b_std) v(V_std) xtitle(Standardized Coefficients) level(95)

. graph export "/Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_evidence.eps", re
> place
(file /Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_evidence.eps written in EPS
>  format)

. 
. 
. * Marginal Effects
. 
. logit scope_broad ictj v2xcs_ccsi_avg5 $violence_type $precedents $domestic_politics yr  precedent_scope, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -46.433398  
Iteration 1:   log pseudolikelihood = -35.095059  
Iteration 2:   log pseudolikelihood = -34.993975  
Iteration 3:   log pseudolikelihood = -34.993838  
Iteration 4:   log pseudolikelihood = -34.993838  

Logistic regression                             Number of obs     =         67
                                                Wald chi2(8)      =      18.52
                                                Prob > chi2       =     0.0176
Log pseudolikelihood = -34.993838               Pseudo R2         =     0.2464

                                        (Std. Err. adjusted for 52 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
       scope_broad |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
              ictj |   2.205221   .9509544     2.32   0.020     .3413845    4.069057
   v2xcs_ccsi_avg5 |  -.3876508   1.596344    -0.24   0.808    -3.516428    2.741126
authoritarian_govt |   .6546492   .8045905     0.81   0.416    -.9223192    2.231618
         civil_war |   .3055294   .6880493     0.44   0.657    -1.043022    1.654081
      transitional |  -.4195703   .6602883    -0.64   0.525    -1.713712    .8745709
         democracy |   .0316941   .7160756     0.04   0.965    -1.371788    1.435176
                yr |   .0591265   .0389529     1.52   0.129    -.0172198    .1354728
   precedent_scope |   .1327462   .2610767     0.51   0.611    -.3789547    .6444471
             _cons |  -2.497309   1.795209    -1.39   0.164    -6.015853    1.021235
------------------------------------------------------------------------------------

. margins, at(ictj=(0 1))

Predictive margins                              Number of obs     =         67
Model VCE    : Robust

Expression   : Pr(scope_broad), predict()

1._at        : ictj            =           0

2._at        : ictj            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3968375   .0793842     5.00   0.000     .2412472    .5524277
          2  |    .826745   .1165152     7.10   0.000     .5983795    1.055111
------------------------------------------------------------------------------

. logit preserve_evidence ictj v2xcs_ccsi_avg5 $violence_type $precedents $domestic_politics yr precedent_preserve_evidence, vce(cluster gwno) /**/

Iteration 0:   log pseudolikelihood = -39.749528  
Iteration 1:   log pseudolikelihood = -28.485415  
Iteration 2:   log pseudolikelihood = -27.759299  
Iteration 3:   log pseudolikelihood = -27.746844  
Iteration 4:   log pseudolikelihood = -27.746827  
Iteration 5:   log pseudolikelihood = -27.746827  

Logistic regression                             Number of obs     =         64
                                                Wald chi2(8)      =      21.11
                                                Prob > chi2       =     0.0069
Log pseudolikelihood = -27.746827               Pseudo R2         =     0.3020

                                                 (Std. Err. adjusted for 51 clusters in gwno)
---------------------------------------------------------------------------------------------
                            |               Robust
          preserve_evidence |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
                       ictj |   1.888463   .9418367     2.01   0.045     .0424971    3.734429
            v2xcs_ccsi_avg5 |  -3.618228   2.417326    -1.50   0.134      -8.3561    1.119644
         authoritarian_govt |   .4853698   .8192137     0.59   0.554     -1.12026    2.090999
                  civil_war |  -1.173035   .9359514    -1.25   0.210    -3.007466    .6613957
               transitional |   1.755142   .9190982     1.91   0.056    -.0462579    3.556541
                  democracy |   2.244277   .9795849     2.29   0.022      .324326    4.164228
                         yr |   .1285906   .0491691     2.62   0.009      .032221    .2249602
precedent_preserve_evidence |   -.233573    .404895    -0.58   0.564    -1.027153    .5600067
                      _cons |   -5.68238   2.251478    -2.52   0.012     -10.0952   -1.269564
---------------------------------------------------------------------------------------------

. margins, at(ictj=(0 1))

Predictive margins                              Number of obs     =         64
Model VCE    : Robust

Expression   : Pr(preserve_evidence), predict()

1._at        : ictj            =           0

2._at        : ictj            =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2067011   .0691657     2.99   0.003     .0711389    .3422633
          2  |   .5242698   .1251539     4.19   0.000     .2789727    .7695669
------------------------------------------------------------------------------

. 
. 
end of do-file

